BACKGROUND: Respiratory system modelling can aid clinical decision making during mechanical ventilation (MV) in intensive care. However, spontaneous breathing (SB) efforts can produce entrained "M-wave" airway pressure waveforms that inhibit identification of accurate values for respiratory system elastance and airway resistance. A pressure wave reconstruction method is proposed to accurately identify respiratory mechanics, assess the level of SB effort, and quantify the incidence of SB effort without uncommon measuring devices or interruption to care. METHODS: Data from 275 breaths aggregated from all mechanically ventilated patients at Christchurch Hospital were used in this study. The breath specific respiratory elastance is calculated using a time-varying elastance model. A pressure reconstruction method is proposed to reconstruct pressure waves identified as being affected by SB effort. The area under the curve of the time-varying respiratory elastance (AUC Edrs) are calculated and compared, where unreconstructed waves yield lower AUC Edrs. The difference between the reconstructed and unreconstructed pressure is denoted as a surrogate measure of SB effort. RESULTS: The pressure reconstruction method yielded a median AUC Edrs of 19.21 [IQR: 16.30-22.47]cmH2Os/l. In contrast, the median AUC Edrs for unreconstructed M-wave data was 20.41 [IQR: 16.68-22.81]cmH2Os/l. The pressure reconstruction method had the least variability in AUC Edrs assessed by the robust coefficient of variation (RCV)=0.04 versus 0.05 for unreconstructed data. Each patient exhibited different levels of SB effort, independent from MV setting, indicating the need for non-invasive, real time assessment of SB effort. CONCLUSION: A simple reconstruction method enables more consistent real-time estimation of the true, underlying respiratory system mechanics of a SB patient and provides the surrogate of SB effort, which may be clinically useful for clinicians in determining optimal ventilator settings to improve patient care. [less ▲]

in Proceedings of the 37th International Conference of the IEEE Engineering in Medicine and Biology Society (2015, August)

Accurate Stroke Volume (SV) monitoring is essential for patient with cardiovascular dysfunction patients. However, direct SV measurements are not clinically feasible due to the highly invasive nature of ... [more ▼]

Accurate Stroke Volume (SV) monitoring is essential for patient with cardiovascular dysfunction patients. However, direct SV measurements are not clinically feasible due to the highly invasive nature of measurement devices. Current devices for indirect monitoring of SV are shown to be inaccurate during sudden hemodynamic changes. This paper presents a novel SV estimation using readily available aortic pressure measurements and aortic cross sectional area, using data from a porcine experiment where medical interventions such as fluid replacement, dobutamine infusions, and recruitment maneuvers induced SV changes in a pig with circulatory shock. Measurement of left ventricular volume, proximal aortic pressure, and descending aortic pressure waveforms were made simultaneously during the experiment. From measured data, proximal aortic pressure was separated into reservoir and excess pressures. Beat-to-beat aortic characteristic impedance values were calculated using both aortic pressure measurements and an estimate of the aortic cross sectional area. SV was estimated using the calculated aortic characteristic impedance and excess component of the proximal aorta. The median difference between directly measured SV and estimated SV was -1.4ml with 95% limit of agreement +/- 6.6ml. This method demonstrates that SV can be accurately captured beat-to-beat during sudden changes in hemodynamic state. This novel SV estimation could enable improved cardiac and circulatory treatment in the critical care environment by titrating treatment to the effect on SV. [less ▲]

in Proceedings of the 37th International Conference of the IEEE Engineering in Medicine and Biology Society (2015, August)

The End-Systolic Pressure-Volume Relation (ESPVR) is generally modelled as a linear relationship between P and V as cardiac reflexes, such as the baroreflex, are typically suppressed in experiments ... [more ▼]

The End-Systolic Pressure-Volume Relation (ESPVR) is generally modelled as a linear relationship between P and V as cardiac reflexes, such as the baroreflex, are typically suppressed in experiments. However, ESPVR has been observed to behave in a curvilinear fashion when cardiac reflexes are not supressed, suggesting the curvilinear function may be more clinically appropriate. Data was gathered from 41 vena cava occlusion manoeuvres performed experimentally at a variety of PEEPs across 6 porcine specimens, and ESPVR determined for each pig. An exponential model of ESPVR was found to provide a higher correlation coefficient than a linear model in 6 out of 7 cases, and a lower Akaike Information Criterion (AIC) value in all cases. Further, the exponential ESPVR provided positive V0 values in a physiological range in6 out of 7 cases analysed, while the linear ESPVR produced positive V0 values in only 3 out of 7 cases, suggesting linear extrapolation of ESPVR to determine V0 may be flawed. [less ▲]

Accurate Stroke Volume (SV) monitoring is essential for patient with cardiovascular dysfunction patients. However, direct SV measurements are not clinically feasible due to the highly invasive nature of ... [more ▼]

Accurate Stroke Volume (SV) monitoring is essential for patient with cardiovascular dysfunction patients. However, direct SV measurements are not clinically feasible due to the highly invasive nature of measurement devices. Current devices for indirect monitoring of SV are shown to be inaccurate during sudden hemodynamic changes. This paper presents a novel SV estimation using readily available aortic pressure measurements and aortic cross sectional area, using data from a porcine experiment where medical interventions such as fluid replacement, dobutamine infusions, and recruitment maneuvers induced SV changes in a pig with circulatory shock. Measurement of left ventricular volume, proximal aortic pressure, and descending aortic pressure waveforms were made simultaneously during the experiment. From measured data, proximal aortic pressure was separated into reservoir and excess pressures. Beat-to-beat aortic characteristic impedance values were calculated using both aortic pressure measurements and an estimate of the aortic cross sectional area. SV was estimated using the calculated aortic characteristic impedance and excess component of the proximal aorta. The median difference between directly measured SV and estimated SV was -1.4ml with 95% limit of agreement +/- 6.6ml. This method demonstrates that SV can be accurately captured beat-to-beat during sudden changes in hemodynamic state. This novel SV estimation could enable improved cardiac and circulatory treatment in the critical care environment by titrating treatment to the effect on SV. [less ▲]

The End-Systolic Pressure-Volume Relation (ESPVR) is generally modelled as a linear relationship between P and V as cardiac reflexes, such as the baroreflex, are typically suppressed in experiments ... [more ▼]

The End-Systolic Pressure-Volume Relation (ESPVR) is generally modelled as a linear relationship between P and V as cardiac reflexes, such as the baroreflex, are typically suppressed in experiments. However, ESPVR has been observed to behave in a curvilinear fashion when cardiac reflexes are not supressed, suggesting the curvilinear function may be more clinically appropriate. Data was gathered from 41 vena cava occlusion manoeuvres performed experimentally at a variety of PEEPs across 6 porcine specimens, and ESPVR determined for each pig. An exponential model of ESPVR was found to provide a higher correlation coefficient than a linear model in 6 out of 7 cases, and a lower Akaike Information Criterion (AIC) value in all cases. Further, the exponential ESPVR provided positive V0 values in a physiological range in6 out of 7 cases analysed, while the linear ESPVR produced positive V0 values in only 3 out of 7 cases, suggesting linear extrapolation of ESPVR to determine V0 may be flawed. [less ▲]

IntroductionTherapeutic hypothermia (TH) is often used to treat out of hospital cardiac arrest (OHCA) patients who also often simultaneously receive insulin for stress-induced hyperglycaemia. However, the ... [more ▼]

IntroductionTherapeutic hypothermia (TH) is often used to treat out of hospital cardiac arrest (OHCA) patients who also often simultaneously receive insulin for stress-induced hyperglycaemia. However, the impact of TH on systemic metabolism and insulin resistance in critical illness is unknown. This study analyses the impact of TH on metabolism, including the evolution of insulin sensitivity (SI) and its variability, in patients with coma after OHCA.MethodsThis study uses a clinically validated, model-based measure of SI. Insulin sensitivity was identified hourly using retrospective data from 200 post-cardiac arrest patients (8,522 hours) treated with TH, shortly after admission to the Intensive Care Unit (ICU). Blood glucose and body temperature readings were taken every one to two hours. Data were divided into three periods: 1) cool (T <35 degrees C); 2) an idle period of two hours as normothermia was re-established; and 3) warm (T >37 degrees C). A maximum of 24 hours each for the cool and warm periods were considered. The impact of each condition on SI is analysed per cohort and per patient for both level and hour-to-hour variability, between periods and in 6-hour blocks.ResultsCohort and per patient median SI levels increase consistently by 35% to 70% and 26% to 59% (P <0.001) respectively from cool to warm. Conversely, cohort and per patient SI variability decreased by 11.1% to 33.6% (P <0.001) for the first 12 hours of treatment. However, SI variability increases between the 18th and 30th hours over the cool-warm transition, before continuing to decrease afterward.ConclusionsOCHA patients treated with TH have significantly lower and more variable SI during the cool period, compared to the later warm period. As treatment continues, SI level rises, and variability decreases consistently except for a large, significant increase during the cool-warm transition. These results demonstrate increased resistance to insulin during mild induced hypothermia. Our study might have important implications for glycaemic control during targeted temperature management. [less ▲]

BACKGROUND: The metabolism of critically ill patients evolves dynamically over time. Post critical insult, levels of counter-regulatory hormones are significantly elevated, but decrease rapidly over the first 12-48 hours in the intensive care unit (ICU). These hormones have a direct physiological impact on insulin sensitivity (SI). Understanding the variability of SI is important for safely managing glycaemic levels and understanding the evolution of patient condition. The objective of this study is to assess the evolution of SI over the first two days of ICU stay, and using this data, propose a separate stochastic model to reduce the impact of SI variability during glycaemic control using the STAR glycaemic control protocol. METHODS: The value of SI was identified hourly for each patient using a validated physiological model. Variability of SI was then calculated as the hour-to-hour percentage change in SI. SI was examined using 6 hour blocks of SI to display trends while mitigating the effects of noise. To reduce the impact of SI variability on achieving glycaemic control a new stochastic model for the most variable period, 0-18 hours, was generated. Virtual simulations were conducted using an existing glycaemic control protocol (STAR) to investigate the clinical impact of using this separate stochastic model during this period of increased metabolic variability. RESULTS: For the first 18 hours, over 80% of all SI values were less than 0.5 x 10(-3) L/mU x min, compared to 65% for >18 hours. Using the new stochastic model for the first 18 hours of ICU stay reduced the number of hypoglycaemic measurements during virtual trials. For time spent below 4.4, 4.0, and 3.0 mmol/L absolute reductions of 1.1%, 0.8% and 0.1% were achieved, respectively. No severe hypoglycaemic events (BG < 2.2 mmol/L) occurred for either case. CONCLUSIONS: SI levels increase significantly, while variability decreases during the first 18 hours of a patients stay in ICU. Virtual trials, using a separate stochastic model for this period, demonstrated a reduction in variability and hypoglycaemia during the first 18 hours without adversely affecting the overall level of control. Thus, use of multiple models can reduce the impact of SI variability during model-based glycaemic control. [less ▲]

in 18th World Congress of the International Federation of Automatic Control (IFAC) (2011)

Effective tight glycemic control (TGC) can improve outcomes in intensive care unit (ICU) <br />patients, but is difficult to achieve consistently. Glycemic level and variability, particularly early in a ... [more ▼]

Effective tight glycemic control (TGC) can improve outcomes in intensive care unit (ICU) <br />patients, but is difficult to achieve consistently. Glycemic level and variability, particularly early in a <br />patient’s stay, are a function of variability in insulin sensitivity/resistance resulting from the level and <br />evolution of stress response, and are independently associated with mortality. This study examines the <br />daily evolution of variability of insulin sensitivity in ICU patients using patient data (N = 394 patients, <br />54019 hours) from the SPRINT TGC study. Model-based insulin sensitivity (SI) was identified each hour <br />and hour-to-hour percent changes in SI were assessed for Days 1-3 individually and Day 4 Onward, as <br />well as over all days. Cumulative distribution functions (CDFs), median values, and inter-quartile points <br />(25th and 75th percentiles) are used to assess differences between groups and their evolution over time. <br />Compared to the overall (all days) distributions, ICU patients are more variable on Days 1 and 2 (p < <br />0.0001), and less variable on Days 4 Onward (p < 0.0001). Day 3 is similar to the overall cohort (p = 0.74). <br />Absolute values of SI start lower and rise for Days 1 and 2, compared to the overall cohort (all days), (p < <br />0.0001), are similar on Day 3 (p = .72) and are higher on Days 4 Onward (p < 0.0001). ICU patients have <br />lower insulin sensitivity (greater insulin resistance) and it is more variable on Days 1 and 2, compared to <br />an overall cohort on all days. This is the first such model-based analysis of its kind. Greater variability <br />with lower SI early in a patient’s stay greatly increases the difficulty in achieving and safely maintaining <br />glycemic control, reducing potential positive outcomes. Clinically, the results imply that TGC patients will <br />require greater measurement frequency, reduced reliance on insulin, and more explicit specification of <br />carbohydrate nutrition in Days 1-3 to safely minimise glycemic variability for best outcome. [less ▲]